def eit_operator(self,input, scale, output = None, mode = "reflect", cval = 0.0):
"""Calculate a multidimensional laplace filter using an estimation
for the second derivative based on differences.
"""
def derivative2(input, axis, output, mode, cval):
return correlate1d(input, scale*np.array([1, -2, 1]), axis, output, mode, cval, 0)
return generic_laplace(input, derivative2, output, mode, cval)
def _scc_single(GT, P, win, ws):
def _scc_filter(inp, axis, output, mode, cval):
return correlate(inp, win, output, mode, cval, 0)
GT_hp = generic_laplace(GT.astype(np.float64), _scc_filter)
P_hp = generic_laplace(P.astype(np.float64), _scc_filter)
win = fspecial(Filter.UNIFORM, ws)
sigmaGT_sq, sigmaP_sq, sigmaGT_P = _get_sigmas(GT_hp, P_hp, win)
sigmaGT_sq[sigmaGT_sq < 0] = 0
sigmaP_sq[sigmaP_sq < 0] = 0
den = np.sqrt(sigmaGT_sq) * np.sqrt(sigmaP_sq)
idx = (den == 0)
den = _replace_value(den, 0, 1)
scc = sigmaGT_P / den
scc[idx] = 0
return scc
def gaussian_laplace(input, sigma):
input = np.asarray(input)
def derivative2(input, axis, output, mode, cval, *extra_arguments,
**extra_keywords):
order = [0] * input.ndim
order[axis] = 2
return gaussian_filter(input, sigma, order)
return generic_laplace(input, derivative2, extra_arguments=(sigma, ))
def eit_operator(self,
input,
scale,
output=None,
mode="reflect",
cval=0.0):
"""Calculate a multidimensional laplace filter using an estimation
for the second derivative based on differences.
"""
def derivative2(input, axis, output, mode, cval):
return correlate1d(input, scale * np.array([1, -2, 1]), axis,
output, mode, cval, 0)
return generic_laplace(input, derivative2, output, mode, cval)
def do_scipy_laplace():
image = cv2.imread(f"sampled_images_interpolated.png")
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
def derivative2(input, axis, output, mode, cval):
return correlate1d(input, [-1, 2, -1], axis, output, mode, cval, 0)
image2 = generic_laplace(image, derivative2, output=None, mode="reflect", cval=0.0)
cv2.imwrite("scipy_laplace.png", image2)
from scipy import ndimage, misc
ascent = misc.ascent()
result = ndimage.laplace(ascent)
cv2.imwrite("ascent.png", ascent)
cv2.imwrite("result.png", result)